Chapter 5 Community composition

5.1 Filter data

load("data/data.Rdata")

Filter samples with high host data

sample_metadata <- sample_metadata %>%
  filter(!sample %in% c("EHI02721", "EHI02712", "EHI02700", "EHI02720", "EHI02749", "EHI02719", "EHI02729", "EHI02715", "EHI02722"))

genome_counts_filt <- genome_counts %>%
  select(one_of(c("genome",sample_metadata$sample)))%>%
  filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
  select_if(~!all(. == 0))
genome_counts <- genome_counts_filt
genome_metadata <- genome_metadata %>% 
  semi_join(., genome_counts_filt, by = "genome") %>% 
  arrange(match(genome,genome_counts_filt$genome))

genome_tree <- keep.tip(genome_tree, tip=genome_metadata$genome) # keep only MAG tips

#load("data/genome_gifts.Rdata")

Make a phyloseq object

phylo_samples <- sample_metadata %>% 
  column_to_rownames("sample") %>% 
  sample_data() #convert to phyloseq sample_data object
phylo_genome <- genome_counts_filt %>% 
  column_to_rownames("genome") %>% 
  otu_table(., taxa_are_rows = TRUE)
phylo_taxonomy <- genome_metadata %>%
  column_to_rownames("genome") %>% 
  as.matrix() %>% 
  tax_table() #convert to phyloseq tax_table object
phylo_tree <- phy_tree(genome_tree) 

physeq_genome <- phyloseq(phylo_genome, phylo_taxonomy, phylo_samples,phylo_tree)
physeq_genome_clr <- microbiome::transform(physeq_genome, 'clr')

save(sample_metadata, 
     genome_metadata, 
     read_counts, 
     genome_counts, 
     genome_counts_filt, 
     genome_tree,
     physeq_genome,
     physeq_genome_clr,
     genome_gifts_raw, 
#     genome_gifts,
     phylum_colors,
     diet_colors,
     file = "data/data_host_filtered.Rdata")

5.2 Load data

load("data/data_host_filtered.Rdata")

5.3 Taxonomy overview

5.3.1 Stacked barplot

genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS normalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  left_join(., sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  filter(diet!="Post_grass") %>% 
  filter(count > 0) %>% #filter 0 counts
  ggplot(., aes(x=sample,y=count, fill=phylum, group=phylum)) + #grouping enables keeping the same sorting of taxonomic units
    geom_bar(stat="identity", colour="white", linewidth=0.1) + #plot stacked bars with white borders
    scale_fill_manual(values=phylum_colors) +
  facet_grid(~factor(diet, labels=c("Pre_grass" = "Captive-born","Wild" = "Wild-born")), scale="free", space = "free")+
 #   facet_nested(. ~ region+diet,  scales="free") + #facet per day and treatment
    guides(fill = guide_legend(ncol = 1)) +
    theme(
          axis.title.x = element_blank(),
          panel.background = element_blank(),
          panel.border = element_blank(),
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank(),
          strip.background = element_rect(fill = "white"),
    strip.text = element_text(size = 12, lineheight = 0.6),
    strip.placement = "outside",
    axis.text.x = element_blank(), axis.ticks.x = element_blank(),
          axis.line = element_line(linewidth = 0.5, linetype = "solid", colour = "black")) +
   labs(fill="Phylum",y = "Relative abundance",x="Samples")

Number of bacteria phyla

[1] 14

Bacteria phyla in wild individuals

[1] 14

Bacteria phyla captive animals

[1] 14

Bacteria phyla before grass is included in the diet

[1] 14

Bacteria phyla after grass is included in the diet

[1] 14

Number of Archaea phyla

[1] 1

Archaea phyla in wild individuals

[1] 0

Archaea phyla before grass is included in the diet

[1] "p__Methanobacteriota"

Archaea phyla after grass is included in the diet

[1] "p__Methanobacteriota"

5.3.2 Genus and species annotation

Number of MAGs without species-level annotation

# A tibble: 1 × 1
  Mag_nospecies
          <int>
1           749

# A tibble: 14 × 4
   phylum               count_nospecies count_total percentage
   <chr>                          <int>       <int>      <dbl>
 1 p__Actinomycetota                 15          15      100  
 2 p__Bacillota                      52          53       98.1
 3 p__Bacillota_A                   516         526       98.1
 4 p__Bacillota_B                     2           2      100  
 5 p__Bacillota_C                     6           9       66.7
 6 p__Bacteroidota                   43         127       33.9
 7 p__Campylobacterota                1           1      100  
 8 p__Cyanobacteriota                 6           7       85.7
 9 p__Desulfobacterota               12          12      100  
10 p__Patescibacteria                13          13      100  
11 p__Pseudomonadota                 32          39       82.1
12 p__Spirochaetota                   2           2      100  
13 p__Synergistota                   18          18      100  
14 p__Verrucomicrobiota              31          35       88.6

Percentage of MAGs without species-level annotation

[1] 87.09302

Number of MAGs without genera-level annotation

79

5.3.3 Phylum relative abundances

phylum_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>%
  left_join(sample_metadata, by = join_by(sample == sample)) %>%
  left_join(genome_metadata, by = join_by(genome == genome)) %>%
  group_by(sample,phylum,region, diet) %>%
  summarise(relabun=sum(count))

phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=mean(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    filter(phylum %in% phylum_arrange) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange))) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[rev(phylum_arrange)]) +
        geom_jitter(alpha=0.5) + 
        theme_minimal() + 
        theme(legend.position="none") +
        labs(y="Phylum",x="Relative abundance")

5.3.3.1 Origin: Wild vs Captive

phylum_summary %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Captive_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Captive_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T)) %>%
    mutate(total=str_c(round(total_mean,3),"±",round(total_sd,3)),
           Wild=str_c(round(Wild_mean,3),"±",round(Wild_sd,3)),
           Captive=str_c(round(Captive_mean,3),"±",round(Captive_sd,3))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total,Wild,Captive)

# A tibble: 15 × 4
   phylum               total         Wild          Captive      
   <chr>                <chr>         <chr>         <chr>        
 1 p__Bacillota_A       57.243±12.618 48.636±13.52  59.846±10.725
 2 p__Bacteroidota      25.787±10.113 26.889±11.805 24.986±10.083
 3 p__Bacillota         4.118±7.202   5.066±11.152  4.949±5.297  
 4 p__Spirochaetota     3.911±10.011  11.731±14.792 0.001±0.001  
 5 p__Verrucomicrobiota 2.264±5.722   1.472±0.715   1.449±1.295  
 6 p__Pseudomonadota    1.626±3.328   0.511±0.397   3.082±5.382  
 7 p__Patescibacteria   0.987±1.542   0.212±0.253   2.177±2.223  
 8 p__Synergistota      0.96±0.852    1.865±0.83    0.45±0.39    
 9 p__Bacillota_C       0.844±0.795   1.696±0.674   0.386±0.36   
10 p__Actinomycetota    0.78±0.985    0.427±0.459   1.217±1.147  
11 p__Cyanobacteriota   0.708±2.081   0.277±0.614   0.916±3.024  
12 p__Desulfobacterota  0.586±0.396   0.991±0.315   0.41±0.256   
13 p__Bacillota_B       0.104±0.145   0.228±0.203   0.042±0.024  
14 p__Methanobacteriota 0.082±0.217   0±0           0.09±0.228   
15 p__Campylobacterota  0±0           0±0           0±0          

5.3.3.2 Origin and diet

phylum_summary %>%
    group_by(phylum) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Pre_grass_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Pre_grass_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Post_grass_mean=mean(relabun[diet=="Post_grass"]*100, na.rm=T),
              Post_grass_sd=sd(relabun[diet=="Post_grass"]*100, na.rm=T))  %>%
    mutate(total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Pre_grass=str_c(round(Pre_grass_mean,6),"±",round(Pre_grass_sd,6)),
           Post_grass=str_c(round(Post_grass_mean,2),"±",round(Post_grass_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(phylum,total,Wild,Pre_grass,Post_grass)

# A tibble: 15 × 5
   phylum               total       Wild        Pre_grass           Post_grass
   <chr>                <chr>       <chr>       <chr>               <chr>     
 1 p__Bacillota_A       57.24±12.62 48.64±13.52 59.845932±10.725422 63.25±9.01
 2 p__Bacteroidota      25.79±10.11 26.89±11.81 24.986055±10.082621 25.49±9.07
 3 p__Bacillota         4.12±7.2    5.07±11.15  4.94862±5.296738    2.34±2.73 
 4 p__Spirochaetota     3.91±10.01  11.73±14.79 0.00063±0.000727    0±0       
 5 p__Verrucomicrobiota 2.26±5.72   1.47±0.71   1.448674±1.294628   3.87±9.89 
 6 p__Pseudomonadota    1.63±3.33   0.51±0.4    3.082488±5.382343   1.29±1.52 
 7 p__Patescibacteria   0.99±1.54   0.21±0.25   2.176993±2.223404   0.57±0.41 
 8 p__Synergistota      0.96±0.85   1.86±0.83   0.449802±0.390105   0.57±0.35 
 9 p__Bacillota_C       0.84±0.79   1.7±0.67    0.385614±0.359883   0.45±0.49 
10 p__Actinomycetota    0.78±0.99   0.43±0.46   1.217389±1.147372   0.7±1.1   
11 p__Cyanobacteriota   0.71±2.08   0.28±0.61   0.915635±3.024426   0.93±1.99 
12 p__Desulfobacterota  0.59±0.4    0.99±0.31   0.410146±0.255958   0.36±0.25 
13 p__Bacillota_B       0.1±0.15    0.23±0.2    0.041532±0.023962   0.04±0.02 
14 p__Methanobacteriota 0.08±0.22   0±0         0.090366±0.227643   0.15±0.29 
15 p__Campylobacterota  0±0         0±0         0.000127±0.000226   0±0       

phylum_arrange <- phylum_summary %>%
    group_by(phylum) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(phylum) %>%
    pull()

phylum_summary %>%
    left_join(genome_metadata %>% select(phylum,phylum) %>% unique(),by=join_by(phylum==phylum)) %>%
#    left_join(sample_metadata,by=join_by(sample==sample)) %>%
    filter(phylum %in% phylum_arrange[1:20]) %>%
    mutate(phylum=factor(phylum,levels=rev(phylum_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=phylum, group=phylum, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~diet)+
        theme_minimal() + 
        labs(y="phylum", x="Relative abundance", color="Phylum")

5.4 Taxonomy boxplot

5.4.1 Family

family_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(., genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,family, diet,region) %>%
  summarise(relabun=sum(count))
family_summary$diet <- factor(family_summary$diet, levels=c("Pre_grass", "Post_grass", "Wild"))

5.4.1.1 Origin: Wild vs Captive

family_summary %>%
    group_by(family) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Cap_mean=mean(relabun[region=="Nafarroa"]*100, na.rm=T),
              Cap_sd=sd(relabun[region=="Nafarroa"]*100, na.rm=T))  %>%
    mutate(Total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Captive=str_c(round(Cap_mean,2),"±",round(Cap_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(family,Total,Wild,Captive) %>% 
    tt()

family	Total	Wild	Captive
f__Lachnospiraceae	21.69±9.91	18.46±10.79	23.3±9.26
f__Bacteroidaceae	17.22±8.91	18.2±9.79	16.73±8.61
f__Ruminococcaceae	10.01±7.82	9.16±9.9	10.44±6.76
f__Oscillospiraceae	7.11±4.79	9.05±6.73	6.14±3.21
f__Borkfalkiaceae	4.87±7.57	1.52±3.18	6.55±8.57
f__Sphaerochaetaceae	3.91±10.01	11.73±14.79	0±0
f__CAG-508	3.24±3.94	0.45±0.37	4.63±4.19
f__Marinifilaceae	2.49±1.42	3.1±1.62	2.19±1.23
f__CAG-74	2.47±2.63	4.7±3.41	1.36±1.04
f__UBA660	2.26±3.79	0.05±0.06	3.37±4.25
f__Acutalibacteraceae	2.14±1.03	1.88±1.17	2.28±0.95
f__Rikenellaceae	2.01±1.42	2.02±1.9	2.01±1.17
f__CAG-314	1.89±4.51	0.04±0.03	2.81±5.32
f__CAG-449	1.69±6.7	5.02±11.16	0.02±0.06
f__Muribaculaceae	1.33±1.73	0.28±0.48	1.85±1.9
f__UBA932	1.26±2.12	2.09±3.28	0.84±1.06
f__Victivallaceae	1.23±0.94	1.41±0.75	1.15±1.02
f__Tannerellaceae	1.2±0.61	1.12±0.79	1.25±0.5
f__Nanosyncoccaceae	0.99±1.54	0.21±0.25	1.37±1.77
f__Akkermansiaceae	0.97±5.81	0±0	1.45±7.12
f__Selenomonadaceae	0.73±0.81	1.69±0.67	0.24±0.23
f__Gastranaerophilaceae	0.71±2.08	0.28±0.61	0.92±2.5
f__CAG-274	0.67±0.66	0.8±0.64	0.6±0.68
f__Dethiosulfovibrionaceae	0.59±0.56	1.25±0.44	0.25±0.19
f__Desulfovibrionaceae	0.59±0.4	0.99±0.31	0.38±0.25
f__Atopobiaceae	0.53±0.85	0.2±0.23	0.69±0.99
f__Burkholderiaceae_A	0.51±0.39	0.46±0.39	0.54±0.4
f__CAG-239	0.49±1.84	0.03±0.1	0.72±2.23
f__CAG-917	0.42±0.68	0±0	0.63±0.75
f__Monoglobaceae	0.41±0.38	0.23±0.39	0.49±0.35
f__Synergistaceae	0.37±0.41	0.61±0.52	0.26±0.29
f__UMGS1883	0.37±0.41	0.19±0.29	0.46±0.43
f__UBA1381	0.36±0.31	0.37±0.22	0.35±0.35
f__UBA1242	0.34±0.65	0.12±0.13	0.46±0.77
f__Anaerovoracaceae	0.23±0.36	0.65±0.35	0.02±0.03
f__Eggerthellaceae	0.23±0.21	0.23±0.24	0.22±0.19
f__CAG-272	0.22±0.28	0.03±0.02	0.32±0.3
f__Pumilibacteraceae	0.21±0.39	0.02±0.03	0.31±0.45
f__Burkholderiaceae_C	0.18±1	0±0	0.26±1.22
f__Butyricicoccaceae	0.15±0.47	0.43±0.76	0.01±0.02
f__UBA1820	0.14±0.15	0.08±0.09	0.17±0.16
f__JAAYOS01	0.13±0.18	0.05±0.07	0.17±0.2
f__Acidaminococcaceae	0.12±0.38	0±0	0.18±0.45
f__Flavobacteriaceae	0.1±0.57	0±0	0.14±0.7
f__Halomonadaceae	0.09±0.29	0±0	0.14±0.35
f__	0.08±0.21	0.17±0.28	0.04±0.15
f__Wohlfahrtiimonadaceae	0.08±0.48	0±0	0.12±0.58
f__Methanobacteriaceae	0.08±0.22	0±0	0.12±0.26
f__UBA644	0.08±0.15	0.02±0.06	0.11±0.18
f__Pseudomonadaceae	0.07±0.41	0±0.01	0.11±0.5
f__Alteromonadaceae	0.07±0.24	0.02±0.04	0.1±0.29
f__Xanthomonadaceae	0.07±0.24	0±0	0.1±0.29
f__Erysipelotrichaceae	0.07±0.15	0±0	0.1±0.18
f__UBA7702	0.06±0.11	0.12±0.17	0.03±0.02
f__UBA1829	0.06±0.12	0.06±0.17	0.06±0.08
f__UBA1750	0.06±0.11	0.15±0.16	0.01±0.03
f__Salinicoccaceae	0.06±0.19	0±0	0.09±0.22
f__Eubacteriaceae	0.05±0.1	0.1±0.15	0.02±0.04
f__Aeromonadaceae	0.05±0.27	0±0	0.07±0.33
f__Peptococcaceae	0.04±0.06	0.1±0.07	0.01±0.01
f__Balneolaceae	0.03±0.14	0±0	0.05±0.17
f__Staphylococcaceae	0.03±0.12	0±0	0.04±0.14
f__Mycobacteriaceae	0.03±0.06	0±0	0.04±0.07
f__Anaerotignaceae	0.02±0.04	0.06±0.05	0±0.01
f__Oleiphilaceae	0.02±0.08	0±0	0.03±0.1
f__Coprobacillaceae	0.02±0.1	0±0	0.03±0.12
f__CAG-382	0.01±0.04	0±0	0.01±0.05
f__Weeksellaceae	0±0	0±0	0±0
f__Neisseriaceae	0±0	0±0	0±0
f__Streptococcaceae	0±0	0±0	0±0
f__Helicobacteraceae	0±0	0±0	0±0
f__Moraxellaceae	0±0	0±0	0±0
f__Pasteurellaceae	0±0	0±0	0±0
f__UBA2023	0±0	0±0	0±0
f__Cardiobacteriaceae	0±0	0±0	0±0

5.4.1.2 Origin and Diet

family_summary %>%
    group_by(family) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Pre_grass_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Pre_grass_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Post_grass_mean=mean(relabun[diet=="Post_grass"]*100, na.rm=T),
              Post_grass_sd=sd(relabun[diet=="Post_grass"]*100, na.rm=T))  %>%
    mutate(Total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Pre_grass=str_c(round(Pre_grass_mean,2),"±",round(Pre_grass_sd,2)),
           Post_grass=str_c(round(Post_grass_mean,2),"±",round(Post_grass_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(family,Total,Wild,Pre_grass,Post_grass) %>% 
    tt()

family	Total	Wild	Pre_grass	Post_grass
f__Lachnospiraceae	21.69±9.91	18.46±10.79	23.69±8.7	22.91±10.16
f__Bacteroidaceae	17.22±8.91	18.2±9.79	16.79±9.7	16.68±7.82
f__Ruminococcaceae	10.01±7.82	9.16±9.9	11.75±8.53	9.12±4.36
f__Oscillospiraceae	7.11±4.79	9.05±6.73	5.21±2.15	7.08±3.87
f__Borkfalkiaceae	4.87±7.57	1.52±3.18	4.6±5.72	8.49±10.62
f__Sphaerochaetaceae	3.91±10.01	11.73±14.79	0±0	0±0
f__CAG-508	3.24±3.94	0.45±0.37	5.81±4.22	3.46±3.98
f__Marinifilaceae	2.49±1.42	3.1±1.62	2.11±1.3	2.27±1.21
f__CAG-74	2.47±2.63	4.7±3.41	1.03±0.72	1.68±1.24
f__UBA660	2.26±3.79	0.05±0.06	4.57±5.23	2.17±2.7
f__Acutalibacteraceae	2.14±1.03	1.88±1.17	2.3±0.79	2.26±1.13
f__Rikenellaceae	2.01±1.42	2.02±1.9	2.14±1.45	1.88±0.83
f__CAG-314	1.89±4.51	0.04±0.03	1.54±2.53	4.08±7.01
f__CAG-449	1.69±6.7	5.02±11.16	0.03±0.08	0±0
f__Muribaculaceae	1.33±1.73	0.28±0.48	1.23±1.33	2.46±2.22
f__UBA932	1.26±2.12	2.09±3.28	1.04±1.21	0.65±0.9
f__Victivallaceae	1.23±0.94	1.41±0.75	1.38±1.25	0.91±0.71
f__Tannerellaceae	1.2±0.61	1.12±0.79	1.17±0.58	1.32±0.42
f__Nanosyncoccaceae	0.99±1.54	0.21±0.25	2.18±2.22	0.57±0.41
f__Akkermansiaceae	0.97±5.81	0±0	0±0	2.9±10.06
f__Selenomonadaceae	0.73±0.81	1.69±0.67	0.21±0.19	0.27±0.28
f__Gastranaerophilaceae	0.71±2.08	0.28±0.61	0.92±3.02	0.93±1.99
f__CAG-274	0.67±0.66	0.8±0.64	0.59±0.5	0.62±0.84
f__Dethiosulfovibrionaceae	0.59±0.56	1.25±0.44	0.19±0.15	0.32±0.21
f__Desulfovibrionaceae	0.59±0.4	0.99±0.31	0.41±0.26	0.36±0.25
f__Atopobiaceae	0.53±0.85	0.2±0.23	0.88±0.98	0.51±1
f__Burkholderiaceae_A	0.51±0.39	0.46±0.39	0.6±0.52	0.47±0.24
f__CAG-239	0.49±1.84	0.03±0.1	0.99±3.04	0.44±1
f__CAG-917	0.42±0.68	0±0	0.61±0.76	0.66±0.77
f__Monoglobaceae	0.41±0.38	0.23±0.39	0.56±0.39	0.42±0.3
f__Synergistaceae	0.37±0.41	0.61±0.52	0.26±0.38	0.25±0.17
f__UMGS1883	0.37±0.41	0.19±0.29	0.31±0.29	0.6±0.51
f__UBA1381	0.36±0.31	0.37±0.22	0.3±0.37	0.41±0.33
f__UBA1242	0.34±0.65	0.12±0.13	0.41±0.58	0.5±0.94
f__Anaerovoracaceae	0.23±0.36	0.65±0.35	0.02±0.02	0.02±0.03
f__Eggerthellaceae	0.23±0.21	0.23±0.24	0.29±0.22	0.16±0.13
f__CAG-272	0.22±0.28	0.03±0.02	0.42±0.39	0.21±0.13
f__Pumilibacteraceae	0.21±0.39	0.02±0.03	0.25±0.29	0.37±0.57
f__Burkholderiaceae_C	0.18±1	0±0	0.53±1.72	0±0.01
f__Butyricicoccaceae	0.15±0.47	0.43±0.76	0.01±0.01	0.02±0.02
f__UBA1820	0.14±0.15	0.08±0.09	0.2±0.19	0.15±0.13
f__JAAYOS01	0.13±0.18	0.05±0.07	0.21±0.25	0.14±0.15
f__Acidaminococcaceae	0.12±0.38	0±0	0.17±0.41	0.18±0.51
f__Flavobacteriaceae	0.1±0.57	0±0	0.28±0.98	0±0
f__Halomonadaceae	0.09±0.29	0±0	0.15±0.36	0.13±0.36
f__	0.08±0.21	0.17±0.28	0.07±0.21	0.01±0.01
f__Wohlfahrtiimonadaceae	0.08±0.48	0±0	0.25±0.82	0±0
f__Methanobacteriaceae	0.08±0.22	0±0	0.09±0.23	0.15±0.29
f__UBA644	0.08±0.15	0.02±0.06	0.09±0.05	0.14±0.25
f__Pseudomonadaceae	0.07±0.41	0±0.01	0.21±0.71	0.01±0.01
f__Alteromonadaceae	0.07±0.24	0.02±0.04	0.12±0.38	0.08±0.18
f__Xanthomonadaceae	0.07±0.24	0±0	0.06±0.16	0.14±0.38
f__Erysipelotrichaceae	0.07±0.15	0±0	0.11±0.18	0.09±0.18
f__UBA7702	0.06±0.11	0.12±0.17	0.03±0.02	0.04±0.02
f__UBA1829	0.06±0.12	0.06±0.17	0.07±0.1	0.06±0.06
f__UBA1750	0.06±0.11	0.15±0.16	0.01±0.01	0.02±0.04
f__Salinicoccaceae	0.06±0.19	0±0	0.1±0.31	0.07±0.09
f__Eubacteriaceae	0.05±0.1	0.1±0.15	0.04±0.05	0±0.01
f__Aeromonadaceae	0.05±0.27	0±0	0.14±0.46	0±0
f__Peptococcaceae	0.04±0.06	0.1±0.07	0.01±0.01	0.01±0.01
f__Balneolaceae	0.03±0.14	0±0	0.02±0.08	0.07±0.23
f__Staphylococcaceae	0.03±0.12	0±0	0.08±0.2	0.01±0.02
f__Mycobacteriaceae	0.03±0.06	0±0	0.05±0.08	0.04±0.05
f__Anaerotignaceae	0.02±0.04	0.06±0.05	0±0.01	0.01±0.01
f__Oleiphilaceae	0.02±0.08	0±0	0.04±0.13	0.02±0.03
f__Coprobacillaceae	0.02±0.1	0±0	0.05±0.17	0±0
f__CAG-382	0.01±0.04	0±0	0.02±0.08	0±0.01
f__Weeksellaceae	0±0	0±0	0±0	0±0
f__Neisseriaceae	0±0	0±0	0±0	0±0
f__Streptococcaceae	0±0	0±0	0±0	0±0
f__Helicobacteraceae	0±0	0±0	0±0	0±0
f__Moraxellaceae	0±0	0±0	0±0	0±0
f__Pasteurellaceae	0±0	0±0	0±0	0±0
f__UBA2023	0±0	0±0	0±0	0±0
f__Cardiobacteriaceae	0±0	0±0	0±0	0±0

family_arrange <- family_summary %>%
    group_by(family) %>%
    summarise(mean=sum(relabun)) %>%
    arrange(-mean) %>%
    select(family) %>%
    pull()

family_summary %>%
    left_join(genome_metadata %>% select(family,phylum) %>% unique(),by=join_by(family==family)) %>%
    filter(family %in% family_arrange[1:20]) %>%
    mutate(family=factor(family,levels=rev(family_arrange[1:20]))) %>%
    filter(relabun > 0) %>%
    ggplot(aes(x=relabun, y=family, group=family, color=phylum)) +
        scale_color_manual(values=phylum_colors[-8]) +
        geom_jitter(alpha=0.5) + 
        facet_grid(.~diet)+
        theme_minimal() + 
        labs(y="Family", x="Relative abundance", color="Phylum")

5.4.2 Genus

genus_summary <- genome_counts_filt %>%
  mutate_at(vars(-genome),~./sum(.)) %>% #apply TSS nornalisation
  pivot_longer(-genome, names_to = "sample", values_to = "count") %>% #reduce to minimum number of columns
  left_join(sample_metadata, by = join_by(sample == sample)) %>% #append sample metadata
  left_join(genome_metadata, by = join_by(genome == genome)) %>% #append genome metadata
  group_by(sample,phylum,genus, diet) %>%
  summarise(relabun=sum(count)) %>%
  filter(genus != "g__") %>%
  mutate(genus= sub("^g__", "", genus))
genus_summary$diet <- factor(genus_summary$diet, levels=c("Pre_grass", "Post_grass", "Wild"))

5.4.3 origin and diet

genus_summary %>%
    group_by(genus) %>%
    summarise(total_mean=mean(relabun*100, na.rm=T),
              total_sd=sd(relabun*100, na.rm=T),
              Wild_mean=mean(relabun[diet=="Wild"]*100, na.rm=T),
              Wild_sd=sd(relabun[diet=="Wild"]*100, na.rm=T),
              Pre_grass_mean=mean(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Pre_grass_sd=sd(relabun[diet=="Pre_grass"]*100, na.rm=T),
              Post_grass_mean=mean(relabun[diet=="Post_grass"]*100, na.rm=T),
              Post_grass_sd=sd(relabun[diet=="Post_grass"]*100, na.rm=T))  %>%
    mutate(Total=str_c(round(total_mean,2),"±",round(total_sd,2)),
           Wild=str_c(round(Wild_mean,2),"±",round(Wild_sd,2)),
           Pre_grass=str_c(round(Pre_grass_mean,2),"±",round(Pre_grass_sd,2)),
           Post_grass=str_c(round(Post_grass_mean,2),"±",round(Post_grass_sd,2))) %>% 
    arrange(-total_mean) %>% 
    dplyr::select(genus,Total,Wild,Pre_grass,Post_grass) %>% 
    tt()

genus	Total	Wild	Pre_grass	Post_grass
Bacteroides	11.45±7.42	10.71±8.63	12.01±7.78	11.63±6.26
Eisenbergiella	9.15±9.45	4.11±11.28	11.85±6.46	11.49±8.57
Phocaeicola	4.75±2.03	5.26±2.32	4.37±2.02	4.64±1.78
UBA9732	3.91±10.01	11.73±14.79	0±0	0±0
Coproplasma	2.59±5.68	0.67±2.29	2.05±3.56	5.05±8.59
Gallimonas	2.28±4.19	0.85±2.4	2.55±3.63	3.44±5.77
UBA7477	2.17±5.94	0.02±0.01	4.95±9.5	1.54±2.86
CAJOIG01	1.97±1.72	2.73±1.76	1.42±1.39	1.77±1.82
Alistipes	1.84±1.42	1.93±1.94	1.91±1.43	1.68±0.74
Fimimonas	1.83±4.48	0.03±0.03	1.47±2.55	4±6.97
Enterenecus	1.78±2.42	3.34±3.63	0.76±0.38	1.23±1.07
UBA1366	1.36±1.8	0.27±0.18	1.68±2.05	2.13±2
Ruminococcus_D	1.3±1.55	0.24±0.28	1.85±1.89	1.81±1.47
CAG-269	1.3±1.53	0.21±0.17	2.39±1.62	1.29±1.47
Egerieousia	1.26±2.12	2.09±3.28	1.04±1.21	0.65±0.9
Gabonibacter	1.24±0.78	1.74±0.87	0.95±0.58	1.04±0.64
Odoribacter	1.22±0.94	1.36±1.26	1.11±0.8	1.2±0.72
Parabacteroides	1.2±0.61	1.12±0.79	1.17±0.58	1.32±0.42
Choladousia	1.17±1.12	1.77±1.17	0.68±0.86	1.05±1.09
UBA3402	1.1±0.68	1.13±0.61	1.12±0.74	1.05±0.74
Nanosyncoccus	0.99±1.54	0.21±0.25	2.18±2.22	0.57±0.41
Akkermansia	0.97±5.81	0±0	0±0	2.9±10.06
Merdiplasma	0.96±1.7	2.87±1.81	0.01±0.01	0.01±0.01
COE1	0.95±1.32	0.75±2.06	1.17±0.92	0.93±0.55
UBA3282	0.9±1.95	0.04±0.03	0.84±1.31	1.81±2.95
Blautia_A	0.86±1.19	1.48±1.84	0.5±0.36	0.6±0.6
RUG626	0.82±1.2	0.17±0.25	1.12±1.01	1.19±1.68
V9D3004	0.82±1.18	1.1±1.85	0.7±0.62	0.66±0.71
Ventricola	0.81±0.91	0.84±0.69	0.51±0.5	1.08±1.32
Victivallis	0.79±0.65	1.03±0.58	0.84±0.82	0.5±0.42
MGBC124762	0.78±1.48	1.46±2.23	0.28±0.42	0.59±0.99
Fimenecus	0.64±0.77	0.2±0.52	1.11±0.9	0.62±0.61
Pyramidobacter	0.59±0.56	1.25±0.44	0.19±0.15	0.32±0.21
Stercorousia	0.58±1.99	0.18±0.57	0.89±3.02	0.67±1.69
CAG-485	0.56±0.61	0.23±0.48	0.56±0.62	0.9±0.56
Paraprevotella	0.55±1.81	1.42±3.02	0.16±0.2	0.08±0.05
Parasutterella	0.51±0.39	0.46±0.39	0.6±0.52	0.47±0.24
Mailhella	0.46±0.41	0.92±0.32	0.23±0.19	0.22±0.21
CALXXL01	0.44±0.39	0.38±0.28	0.54±0.52	0.41±0.34
Avilachnospira	0.44±0.99	1.31±1.36	0±0	0.02±0.04
Choladocola	0.44±0.55	0.22±0.6	0.59±0.58	0.5±0.45
Ruminococcus	0.43±0.62	0.33±0.33	0.34±0.27	0.61±0.99
CAG-590	0.42±0.94	0.04±0.09	0.75±1.28	0.47±0.94
Roseburia	0.42±0.87	0.08±0.11	0.43±0.67	0.73±1.31
UBA3855	0.41±0.57	0.29±0.65	0.34±0.39	0.6±0.64
HGM10766	0.41±2.16	0±0	1.19±3.72	0.02±0.03
Monoglobus	0.41±0.38	0.23±0.39	0.56±0.39	0.42±0.3
MGBC105563	0.4±0.77	0.12±0.24	0.63±0.89	0.43±0.94
UMGS1601	0.4±0.65	0.15±0.29	0.32±0.37	0.72±0.97
Pelethomonas	0.39±0.43	0.27±0.63	0.35±0.21	0.54±0.31
Scatovivens	0.38±0.94	0±0	0.86±1.45	0.28±0.56
Caccocola	0.37±0.41	0.61±0.52	0.26±0.38	0.25±0.17
Gemmiger	0.37±0.92	0.53±1.18	0.36±1.04	0.21±0.42
Phocaeicola_A	0.35±0.34	0.5±0.45	0.24±0.19	0.32±0.31
CAG-452	0.34±0.93	0.02±0.03	0.86±1.48	0.15±0.32
Muribaculum	0.34±1.08	0.01±0.01	0.4±1.09	0.6±1.53
UMGS1663	0.34±0.87	0.05±0.04	0.6±1.29	0.37±0.73
14-2	0.32±1.87	0±0.01	0.94±3.24	0.01±0
CAJMNU01	0.3±1.8	0±0	0.9±3.12	0.01±0
UBA1213	0.3±0.54	0.09±0.24	0.27±0.31	0.54±0.82
UBA6857	0.29±0.41	0.7±0.51	0.08±0.05	0.08±0.04
UBA1224	0.29±0.73	0.85±1.09	0.01±0.01	0.01±0.01
CALWRD01	0.29±0.32	0.06±0.09	0.39±0.34	0.41±0.35
Paramuribaculum	0.26±0.38	0.04±0.02	0.26±0.25	0.49±0.52
JAJQCX01	0.25±0.32	0.02±0.01	0.24±0.22	0.49±0.39
Marvinbryantia	0.25±0.35	0.06±0.02	0.42±0.43	0.26±0.38
SIG200	0.23±0.79	0±0	0.54±1.29	0.16±0.41
SFTH01	0.23±0.35	0.47±0.5	0.11±0.11	0.1±0.14
Aphodocola	0.22±0.79	0±0	0.61±1.3	0.06±0.18
Limadaptatus	0.21±0.49	0±0	0.38±0.7	0.26±0.41
CAG-475	0.21±0.52	0±0	0.23±0.51	0.39±0.72
Copromonas	0.21±0.22	0.24±0.32	0.13±0.12	0.25±0.18
CAG-95	0.21±0.97	0.01±0.01	0.07±0.05	0.54±1.67
Schaedlerella	0.2±0.47	0.02±0.01	0.15±0.15	0.44±0.75
Lawsonibacter	0.2±0.26	0.39±0.27	0.09±0.2	0.11±0.22
Eubacterium_R	0.19±0.34	0.03±0.01	0.32±0.39	0.24±0.4
RGIG2066	0.19±0.4	0.58±0.51	0±0	0±0
CAG-552	0.19±0.31	0.01±0.03	0.25±0.29	0.29±0.41
CAG-353	0.18±0.86	0±0	0.41±1.42	0.14±0.48
Acetatifactor	0.18±0.21	0.06±0.04	0.26±0.31	0.23±0.15
RUG115	0.18±0.47	0.34±0.8	0.12±0.08	0.09±0.06
Marseille-P3106	0.17±0.37	0.02±0.04	0.22±0.3	0.28±0.55
Alistipes_A	0.17±0.12	0.09±0.08	0.23±0.1	0.2±0.13
UMGS2069	0.17±0.18	0.12±0.14	0.21±0.27	0.17±0.11
Dysosmobacter	0.16±0.16	0.24±0.22	0.13±0.12	0.12±0.08
Duncaniella	0.16±0.93	0±0	0.01±0.02	0.48±1.61
Eubacterium_I	0.16±0.28	0.44±0.35	0.01±0.01	0.02±0.03
Eubacterium_G	0.15±0.27	0.01±0.01	0.28±0.39	0.17±0.2
Pseudoruminococcus	0.15±0.34	0±0	0.2±0.37	0.26±0.43
Ventrimonas	0.15±0.27	0.29±0.37	0.12±0.24	0.03±0.02
UMGS995	0.15±0.52	0±0	0.4±0.87	0.04±0.07
Paenalcaligenes	0.14±0.81	0±0	0.43±1.4	0±0.01
Merdimorpha	0.14±0.15	0.08±0.09	0.2±0.19	0.15±0.13
CAG-103	0.14±0.49	0±0	0.13±0.39	0.3±0.75
MGBC163016	0.14±0.15	0.05±0.04	0.23±0.19	0.15±0.13
CAG-793	0.14±0.34	0.02±0.02	0.23±0.42	0.17±0.4
RGIG3926	0.14±0.35	0.41±0.53	0±0	0±0
CAG-41	0.14±0.26	0.03±0.05	0.19±0.35	0.19±0.27
CALXFK01	0.13±0.18	0.05±0.07	0.21±0.25	0.14±0.15
Onthocola_B	0.13±0.42	0±0	0.38±0.68	0.01±0.02
Butyricicoccus_A	0.13±0.47	0.36±0.78	0.01±0.01	0.01±0.02
Coprococcus	0.13±0.33	0.01±0.02	0.11±0.09	0.27±0.55
UBA5578	0.13±0.58	0±0.02	0.08±0.19	0.29±0.99
Caccovivens	0.12±0.36	0±0	0.2±0.44	0.18±0.43
RUG11788	0.12±0.37	0±0	0.08±0.19	0.28±0.59
CAG-266	0.12±0.38	0±0	0.17±0.41	0.18±0.51
Mediterranea	0.11±0.6	0.31±1.03	0.01±0.01	0.01±0.01
Fimivicinus	0.11±0.43	0.01±0.02	0.01±0	0.32±0.71
Acutalibacter	0.11±0.15	0.08±0.05	0.11±0.16	0.13±0.19
UBA11774	0.1±0.37	0.26±0.62	0.03±0.02	0.02±0.02
Hominisplanchenecus	0.1±0.08	0.13±0.06	0.1±0.11	0.08±0.05
CALXJL01	0.1±0.54	0±0	0.3±0.93	0.01±0.01
Ventrenecus	0.1±0.38	0±0	0.28±0.63	0.01±0.01
Flavobacterium	0.1±0.57	0±0	0.28±0.98	0±0
Halomonas	0.09±0.29	0±0	0.15±0.36	0.13±0.36
UBA738	0.09±0.33	0.27±0.55	0±0	0.01±0
JAAWPK01	0.09±0.27	0.12±0.26	0.13±0.39	0.03±0.05
RGIG2774	0.09±0.3	0.25±0.49	0±0	0±0
UBA3789	0.09±0.34	0±0	0.03±0.03	0.23±0.58
UMGS973	0.08±0.17	0.22±0.24	0.01±0.02	0.02±0.03
CALWPC01	0.08±0.17	0.19±0.26	0.04±0.05	0.02±0.01
Methanosphaera	0.08±0.22	0±0	0.09±0.23	0.15±0.29
Geddesella	0.08±0.15	0.02±0.06	0.09±0.05	0.14±0.25
UMGS1124	0.08±0.44	0±0	0±0.01	0.23±0.76
Butyribacter	0.08±0.11	0.01±0.01	0.14±0.13	0.08±0.12
Limivicinus	0.08±0.21	0±0	0.07±0.16	0.15±0.31
F23-B02	0.08±0.26	0±0	0.01±0.02	0.22±0.43
Zag111	0.07±0.43	0±0	0±0.01	0.22±0.74
Avispirillum	0.07±0.11	0±0	0.13±0.15	0.08±0.07
UBA11524	0.07±0.3	0±0	0.03±0.05	0.18±0.52
Aliidiomarina	0.07±0.24	0.02±0.04	0.12±0.38	0.08±0.18
Thiopseudomonas	0.07±0.41	0±0	0.21±0.71	0±0
Faecousia	0.07±0.25	0.01±0.01	0.19±0.42	0.01±0.01
Ignatzschineria	0.07±0.4	0±0	0.2±0.68	0±0
Luteimonas_D	0.07±0.24	0±0	0.06±0.16	0.14±0.38
Cryptoclostridium	0.06±0.11	0.12±0.17	0.03±0.02	0.04±0.02
CAG-115	0.06±0.34	0±0	0.18±0.59	0±0
UBA11452	0.06±0.12	0.06±0.17	0.07±0.1	0.06±0.06
UBA1428	0.06±0.11	0.15±0.16	0.01±0.01	0.02±0.04
RUG11130	0.06±0.15	0.02±0.02	0.12±0.25	0.04±0.03
JAFLUQ01	0.06±0.18	0.17±0.29	0.01±0.01	0±0
RUG14903	0.06±0.11	0.01±0.01	0.13±0.16	0.04±0.04
CAG-273	0.06±0.24	0±0	0.15±0.41	0.01±0.02
12844	0.06±0.15	0.01±0.02	0.04±0.05	0.12±0.24
Onthoplasma	0.05±0.32	0±0	0±0	0.16±0.55
Enterocloster	0.05±0.09	0.06±0.15	0.05±0.05	0.05±0.05
RUG591	0.05±0.3	0±0	0.15±0.53	0±0
Heteroclostridium	0.05±0.06	0.01±0.01	0.07±0.06	0.08±0.06
UBA2882	0.05±0.27	0±0	0.14±0.47	0.02±0.06
JAAWJJ01	0.05±0.12	0.15±0.17	0±0	0±0
CAKSQF01	0.05±0.08	0±0	0.08±0.09	0.06±0.09
Scatocola	0.05±0.22	0.03±0.1	0.11±0.37	0±0
Eubacterium_F	0.05±0.12	0.01±0.01	0.1±0.19	0.04±0.07
Ornithomonoglobus	0.05±0.14	0.13±0.23	0.01±0.02	0±0.01
Emergencia	0.05±0.08	0.13±0.08	0±0	0±0
UBA7067	0.05±0.1	0.02±0.05	0.05±0.08	0.06±0.15
Oceanisphaera	0.05±0.27	0±0	0.14±0.46	0±0
Ruminococcus_E	0.04±0.15	0.08±0.23	0.05±0.14	0.01±0.01
RUG12438	0.04±0.18	0±0	0.03±0.07	0.1±0.3
Caccovicinus	0.04±0.21	0±0.01	0.11±0.36	0.01±0.01
Ruminococcus_C	0.04±0.08	0.03±0.1	0.05±0.05	0.04±0.09
RGIG7179	0.04±0.08	0.12±0.09	0±0	0±0
UBA7185	0.04±0.06	0.1±0.07	0.01±0.01	0.01±0.01
UBA1066	0.04±0.09	0.12±0.12	0±0	0±0
Ruminiclostridium_E	0.04±0.18	0±0	0±0	0.11±0.31
Blautia	0.04±0.19	0±0	0.01±0.01	0.1±0.33
RUG11890	0.04±0.14	0±0	0.04±0.07	0.07±0.23
RUG762	0.04±0.17	0±0.01	0.09±0.3	0.01±0
Faecalimonas	0.03±0.11	0.1±0.17	0±0	0±0
MGBC119817	0.03±0.07	0±0	0.07±0.11	0.03±0.05
Scybalousia	0.03±0.16	0±0	0.08±0.28	0.02±0.07
Alangreenwoodia	0.03±0.06	0.1±0.06	0±0	0±0
Ruthenibacterium	0.03±0.05	0±0.01	0.05±0.07	0.04±0.05
UBA3818	0.03±0.05	0±0	0.03±0.02	0.07±0.06
Jeotgalicoccus	0.03±0.16	0±0	0.09±0.27	0.01±0.02
Oligella	0.03±0.18	0±0	0.09±0.32	0±0.01
Coprovivens	0.03±0.19	0±0	0±0	0.09±0.33
UBA2664	0.03±0.14	0±0	0.02±0.08	0.07±0.23
Bilophila	0.03±0.02	0.05±0.02	0.02±0.01	0.02±0.01
MGBC100174	0.03±0.06	0.03±0.07	0.02±0.03	0.04±0.06
Avigastranaerophilus	0.03±0.17	0.09±0.29	0±0	0±0
Butyricimonas	0.03±0.07	0.01±0	0.05±0.11	0.03±0.03
Staphylococcus	0.03±0.12	0±0	0.08±0.2	0.01±0.02
Corynebacterium	0.03±0.06	0±0	0.05±0.08	0.04±0.05
RGIG3102	0.03±0.06	0.08±0.09	0±0	0±0
JABUSF01	0.03±0.07	0±0	0.04±0.06	0.04±0.1
Salinicoccus	0.03±0.06	0±0	0.02±0.04	0.06±0.08
Protoclostridium	0.03±0.15	0±0	0±0	0.08±0.26
QAKD01	0.03±0.08	0.08±0.12	0±0	0±0
UBA3305	0.02±0.11	0.06±0.18	0.01±0.01	0.01±0
RGIG1902	0.02±0.04	0.06±0.04	0±0	0±0
Metalachnospira	0.02±0.04	0.06±0.05	0±0.01	0.01±0.01
CADBMC01	0.02±0.04	0.06±0.06	0.01±0.01	0±0.01
Angelakisella	0.02±0.05	0.07±0.06	0±0	0±0
SIG32	0.02±0.04	0±0	0.02±0.02	0.04±0.06
Faecivivens	0.02±0.04	0.06±0.06	0±0	0±0
MGBC108787	0.02±0.11	0±0	0±0	0.06±0.19
Lachnoclostridium_B	0.02±0.06	0±0	0.05±0.11	0.01±0.01
TWA4	0.02±0.03	0.04±0.04	0.01±0.01	0.01±0.01
CAKOLD01	0.02±0.1	0±0	0.06±0.16	0±0
HGM12545	0.02±0.1	0±0	0.05±0.16	0.01±0.02
Firm-04	0.02±0.08	0.04±0.14	0.01±0.02	0.01±0.01
HGM13634	0.02±0.09	0±0	0.05±0.16	0.01±0.02
Marinobacter	0.02±0.08	0±0	0.04±0.13	0.02±0.03
KLE1615	0.02±0.05	0.05±0.08	0±0	0±0
MGBC113645	0.02±0.1	0±0	0.05±0.17	0±0
CAKSEI01	0.02±0.04	0±0	0.04±0.06	0.01±0.01
UMGS1994	0.02±0.05	0±0	0.04±0.09	0.01±0.01
Fusicatenibacter	0.02±0.05	0±0	0±0.01	0.05±0.07
Wohlfahrtiimonas	0.01±0.08	0±0	0.04±0.14	0±0
Onthousia	0.01±0.08	0±0	0.04±0.13	0±0
UMGS1754	0.01±0.06	0±0	0.03±0.1	0±0
Caccenecus	0.01±0.06	0±0	0.03±0.09	0.01±0.02
UBA1752	0.01±0.04	0±0	0.02±0.08	0±0.01
CAG-272	0.01±0.05	0±0	0.02±0.08	0±0
Pseudomonas	0.01±0.01	0±0.01	0±0	0.01±0.01
JABCPE02	0±0	0±0	0±0	0±0
Streptococcus	0±0	0±0	0±0	0±0
Alysiella	0±0	0±0	0±0	0±0
Neisseria	0±0	0±0	0±0	0±0
HGM08974	0±0	0±0	0±0	0±0
Pelistega	0±0	0±0	0±0	0±0
Moraxella	0±0	0±0	0±0	0±0
Actinobacillus	0±0	0±0	0±0	0±0
GN02-873	0±0	0±0	0±0	0±0

genus_arrange <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=sum(relabun)) %>%
    filter(genus != "g__")%>%
    arrange(-mean) %>%
    select(genus) %>%
    mutate(genus= sub("^g__", "", genus)) %>%
    pull()

genus_summary_sort <- genus_summary %>%
    group_by(genus) %>%
    summarise(mean=mean(relabun, na.rm=T),sd=sd(relabun, na.rm=T)) %>%
    arrange(-mean) 

genus_summary %>%
  mutate(genus=factor(genus, levels=rev(genus_summary_sort %>% pull(genus)))) %>%
  filter(relabun > 0) %>%
  ggplot(aes(x=relabun, y=genus, group=genus, color=phylum)) +
  scale_color_manual(values=phylum_colors) +
  geom_jitter(alpha=0.5) + 
  facet_grid(.~diet)+
  theme_minimal() + 
  theme(axis.text.y = element_text(size=6))+
  labs(y="Family", x="Relative abundance", color="Phylum")